Ford v Ferrari : ESG v AI

Social and environmental issues have received a sparkling new paint job, and a renewed focus for businesses in the form of ESG (environmental, social, and governance). On the other side of the track recent developments in artificial intelligence (AI), specifically generative AI has created a new energy and excitement for what lays ahead.

Businesses are racing to understand how they can leveraging AI capabilities to support their operations, whilst also continue to value their ESG commitments.

In a survey conducted by KPMG international, 44% of CEO’s said that ESG programs improve their financial performance. Business leaders want to accelerate their ESG agenda, not only through products and investments, but also within their own organisations aligning to the values of their customers.

In the red Ferrari growling next to ESG is generative AI. With several exciting new models and the ability to process vast amounts of data, identify patterns, and make predictions, AI has risen to the top of a CEOs transformation wish list. In fact, 70% of CEO's have sighted generative AI as their primary investment focus. However, as businesses race to embrace the new technology, a crucial consideration is emerging.

At the crossroad of AI and ESG lies a complex challenge that needs to be addressed. On one hand, AI presents a powerful enabler carving tracks into a more sustainable future. On the other, the very technologies driving AI's models carries a significant environmental burden that could undermine corporate ESG commitments all together.

AI supporting ESG’s podium finish

AI can be a powerful partner for ESG, enabling a wealth of applications to support climate action, environmental sustainability, and progress towards a low-carbon economy.

1.?Accelerating the transition to a low-carbon economy

AI’s capabilities in gathering and analysing data on emissions and climate effects can inform policy decisions, climate risk analysis, and optimise operating models and supply chains. AI-powered solutions are already being leveraged to increase the economic value of wind energy by up to 20% through advanced forecasting and grid balancing capabilities.

In the motor industry, machine learning is optimising route planning and traffic monitoring, reducing emissions from vehicles by reducing the miles travelled with each journey.

2. Driving sustainable farming

Agriculture stands to benefit greatly from AI-driven solutions. Precision agriculture tools, intelligent irrigation systems, and crop monitoring capabilities enabled by AI can significantly improve resource efficiency and crop resilience. X’s Project Mineral has developed a low-emission electric power rover fitted with solar panels that uses GPS software to identify the location of plants in the field and then applies machine learning tools to analyse the data and identify the resilience and productivity of crops in different environments. This will lead to greater crop yields and more sustainable farming.

3. Minimising the impact of climate change

AI Powered hazard forecasting and early warning systems can enhance preparedness for climate-related disasters, potentially saving lives and minimizing economic losses. Deep-learning algorithms can improve the sensitivity and specificity of early warning systems about climate tipping points. This can be used to project long-term trends in a particular region (e.g. modelling sea-level rise) to help people prepare for the impact of major climate changes.

Pulling up the hand break: AI's environmental impact

While AI presents promising opportunities for ESG advancement, its growing energy demands and associated environmental impact pose a significant challenge. The heart of this dilemma lies in the staggering computational power required to train and operate advanced AI models, particularly the use of generative AI. With the growing adoption of AI across industries, the energy consumption of these technologies is soaring, putting pressure on corporate sustainability goals.

"There's a real tension here," says Sarah Hendriks, an ESG analyst at a leading investment firm. "Companies are being pushed to embrace AI to stay competitive, but the energy demands of these technologies can undermine their environmental commitments. It's a delicate balancing act."

Training a single AI model can generate as much carbon dioxide as several cars over their lifetime. As AI models become more complex, their energy footprint is expected to grow exponentially. The energy-intensive nature of AI directly clashes with corporate commitments to reduce carbon emissions and meet net-zero targets.

One assessment suggests that ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes. It’s estimated that a search driven by generative AI uses four to five times the energy of a conventional web search. Within years, large AI systems are likely to need as much energy as entire nations.

In addition, the data centres that house the powerful computing infrastructure underpinning AI also contributes to the environmental burden. These facilities not only consume vast amounts of electricity but also require significant water resources for cooling purposes. Data centres are estimated to account for around 1% of global electricity consumption, and this share is projected to rise significantly as AI scales up.

As Google and Microsoft prepared their Bard and Bing large language models, both had major spikes in water use — increases of 20% and 34%, respectively, in one year, according to the companies’ environmental reports. By 2027, the demand of water to cool down AI models could be half that of the United Kingdom.

AI design must be steered by ESG

Artificial intelligence (AI) has both positive and negative impacts on the environment. On one hand, AI can help predict and limit the impacts of climate change, design more energy-efficient buildings, and optimise renewable energy deployment. On the other hand, the environmental costs of AI, particularly generative AI, are predicted to increase to unmanageable levels. Company’s need to adopt design practices to limit AI’s ecological impact. A multifaceted approach is needed to drive AI's potential while minimising its environmental impact.

1. Prioritising sustainable AI design

The first step in reconciling AI and ESG, is to embed sustainability considerations into the core of an AI models design. Implementing ESG by design involves prioritising energy efficiency from the outset, exploring renewable energy sources to power data centres, and implementing comprehensive environmental impact assessments.

Companies can draw inspiration from initiatives like the BigScience project in France, which demonstrated the feasibility of building a large language model comparable to OpenAI's GPT-3 with a significantly lower carbon footprint. By prioritizing sustainable practices and embracing energy-efficient hardware, algorithms, and data centre designs, companies can harness the power of AI while minimizing its environmental costs.

2. Transparency and honest reporting

Fostering transparency and accountability is essential in this journey. Companies should measure and publicly report their AI-related energy and water consumption, enabling stakeholders to assess progress towards sustainability goals. Regular environmental reporting can further support adherence to standards and best practices.

3. Rules and Regulation

Regulators have a pivotal role to play in shaping the AI-ESG landscape. Comprehensive legislation, such as the proposed Artificial Intelligence Environmental Impacts Act, can set benchmarks for energy and water use, incentivise the adoption of renewable energy sources, and mandate comprehensive environmental reporting and impact assessments.

By fostering a supportive regulatory environment that encourages sustainable AI practices, policymakers can create a level playing field and drive industry-wide progress towards aligning AI ambitions with ESG commitments.

The road forward adopting sustainable AI

The road ahead will not be without challenges, but the rewards of aligning AI ambitions with ESG goals are immense. Companies that embrace sustainable AI practices will not only contribute to a more sustainable future but also position themselves as leaders in a rapidly evolving technological landscape, resonating with stakeholders and consumers who increasingly prioritize environmental and social responsibility.

In the words of Dr. Emily Chen, a leading expert in AI ethics and governance, "The key to reconciling AI and ESG lies in a more holistic approach to sustainability. Companies need to consider the full life cycle of their AI systems, from development to deployment and beyond. They need to optimize for energy efficiency, explore renewable energy sources, and find ways to offset the carbon footprint of these technologies."

The AI revolution is upon us, and the impact on the environment will be shaped by the choices we make today. By embracing sustainable AI design and aligning technological improvements with ESG commitments, we can unlock the full potential of AI while protecting the well-being of our planet.

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